Compact implementations for limited-resource platforms

ABSTRACT

Methods and systems to enable simplified, compact implementations of profiling and other functions on STBs or other limited-resource computing platforms. For every television show transmitted from the server to the STB, the server also sends a classifying group identifier for the show. The STB then generates user profiles, ratings and/or recommendations, based on the group identifiers, simplifying the STB&#39;s processing tasks. Exception processing, special cases, or changes in group assignments or algorithms are handled at the server. The STB calculates viewing profiles based on recording statistics about content its user(s) view, based on the group identifiers. In another embodiment, the STB provides ratings and/or recommendations using group identifier information and related statistics. User privacy is maintained by stripping off and discarding personal information or averaging user profile information to erase or “blur” any suggestions of personal information.

PRIORITY CLAIM

[0001] The present application claims priority to co-pending ProvisionalApplication Serial No. 60/360,089 entitled “Compact Implementations forLimited-Resource Platforms”, filed on Feb. 25, 2002, and having a commoninventive entity.

INCORPORATION BY REFERENCE

[0002] The present application for United States patent incorporates byreference the following commonly-owned patent applications, as if setforth in their entirety herein, for all purposes:

[0003] WO 0120481A2 {Predictive Networks PCT application};

[0004] PCT Application No. PCT/US02/______ entitled “Privacy-MaintainingMethods and Systems For Collecting Information” filed May 15, 2002;

[0005] U.S. Patent Application No. 60/338,398 filed Dec. 7, 2001;

[0006] U.S. patent application entitled: “Television Program NavigationGuide” filed Dec. 5, 2001;

[0007] U.S. patent application entitled: “Method and System forSelective Initial Television Channel Display” filed Oct. 22, 2001;

[0008] U.S. patent application Ser. No. 09/969,911 filed Oct. 3, 2001;

[0009] U.S. patent application entitled: “Method and System for ParsingPurchase Information from Web Pages filed Aug. 29, 2001;

[0010] U.S. patent application Ser. No. 09/928,493 filed Aug. 13, 2001;

[0011] U.S. patent application Ser. No. 09/877,974 filed Jun. 7, 2001;

[0012] U.S. patent application Ser. No. 09/558,755 filed Apr. 21, 2001;

[0013] U.S. patent Application Ser. No. 60/282,028 filed Apr. 6, 2001;

[0014] U.S. patent application Ser. No. 09/798,337 filed Mar. 2, 2001;

[0015] U.S. patent application Ser. No. 09/777,807 filed Feb. 5, 2001;

[0016] U.S. patent application Ser. No. 09/767,693 filed Jan. 23, 2001;and

[0017] U.S. patent application Ser. No. 09/766,377 filed Jan. 19, 2001.

BACKGROUND OF THE INVENTION

[0018] With hundreds of TV channels and scheduled programs from which tochoose, together with personal video recorder (PVR)-recorded shows,pay-per-view (PPV), video-on-demand (VOD) and other content, TV viewersand other content users are faced with a nearly overwhelming choice ofentertainment and other content options.

[0019] In response, various electronic or interactive programming guide(EPG/IPG) systems have been proposed or developed to enhance TV viewers'ability to navigate through and select programming. Examples of suchsystems are set forth in the following U.S. and foreign patentdocuments, among others, the disclosures of which are incorporatedherein by reference as if set forth in their entirety here:

[0020] U.S. Pat. No. 6,177,931 Alexander et al.

[0021] U.S. Pat. No. 6,163,316 Killian

[0022] U.S. Pat. No. 6,005,597 Barrett et al.

[0023] WO 0049801A1 Yuen et al.

[0024] WO 0033224A1 Yuen

[0025] Many such systems, including those disclosed in the listeddocuments, use profiling processes to develop information about viewersand thereby provide content ratings or recommendations. Typicalprofiling processes, however, are relatively complex, and requiresignificant computational resources. This, in turn, necessitates complexand expensive set-top box (STB) hardware.

[0026] It is therefore desirable to provide methods and systems thatenable compact implementations of profiling and other functions onlimited-resource platforms such as set-top boxes (STBs) and the like.

[0027] It is also desirable to employ methods and systems that enablethe collection of information for collaborative filtering and otherlegitimate purposes, utilizing compact implementations of profiling andother functions, on limited-resource platforms such as set-top boxes(STBs) and the like, to generate multi-dimensional user profiles thatavoid the transmission or storage of private, identifiable, personalinformation.

SUMMARY OF THE INVENTION

[0028] The invention provides methods and systems to enable simplified,compact implementations of profiling and other functions on STBs orother limited-resource computing platforms. The invention is based onthe principle that for every television show transmitted from the serverto the STB, the server also sends associated “group numbers” indicating,for example, that “the current show belongs to Group No. 20 (sports) andGroup No. 35 (football).” The STB can then execute its processing, suchas the generation of user profiles, ratings and/or recommendations,based on the group numbers. Because of the information implicit in thegroup numbers, the STB's processing tasks are greatly simplified.Exception processing, special cases, or changes in group assignments oralgorithms can be handled at the server. It will be appreciated thatnon-numerical identifiers, and indeed, any form of identifiers can beused in place of numbers. These may include letters or other symbols asappropriate.

[0029] The STB can calculate profiles, for example, based on simplyobserving and recording statistics about content its user(s) view, basedon the group numbers. For example, the STB can easily determine thefollowing: “How often does viewer X watch programs in group Y?”Optionally, the STB could also provide ratings and/or recommendationsusing group number information and related statistics.

[0030] The present invention further provides methods and systems forcollecting and aggregating information from user terminals, such asset-top boxes (STBs) that may employ electronic programming guide (EPG)features, without the necessity of collecting or storing personal,private user information. The invention also enables collaborativefiltering and the generation of recommended future decisions based onanonymous user profile information.

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Features and advantages of the present invention will becomeapparent to those skilled in the art from the description below, withreference to the following drawing figures, in which:

[0032]FIG. 1 is a schematic diagram of a content distribution system inwhich the present invention may be deployed.

[0033]FIG. 2 is a flowchart illustrating method steps of one practice ofthe present invention.

[0034]FIG. 3 is a flowchart illustrating method steps of one practice ofthe present invention.

DESCRIPTION OF ILLUSTRATED EMBODIMENTS

[0035] The invention provides methods and systems enabling compactimplementations of profiling and other functions on STBs and otherlimited-resource computing platforms.

[0036] Prior Art Systems: As shown in FIG. 1, the methods and systems ofthe invention are advantageously deployed in an otherwise conventional,networked content distribution system 100, such as a television contentdistribution system including a conventional server 102 and at least oneconventional STB 104. The server 102 and STBs 104 can be constructed inaccordance with known principles such as those disclosed in theabove-listed patent documents, which are incorporated herein byreference in their entirety as if set forth fully herein. In general, asshown in FIG. 2, the server 102 is operable to send content to the STBs104, and the STBs 104 can send requests, acknowledgements and otherinformation to the server.

[0037] Present Invention: FIG. 2 shows method steps executed inaccordance with one practice of the present invention, as deployed in atelevision content distribution system like that shown in FIG. 1.

[0038] As illustrated in FIG. 2, the server 102 first determines:

[0039] (1) a collection of “groups” to which various television shows orother content may be assigned (e.g., by genre, such as sports, comedyand the like), and optionally a weighting factor for each group in astep 202;

[0040] (2) for each television show or other content, a groupidentification (ID) value of the group or groups to which the contentmay be assigned (e.g., groups 1, 2 and 7) in a step 204; and

[0041] (3) optionally, a weighting factor indicating the desirability ofrepeats for the show further in step 204.

[0042] Next, the server broadcasts this information to the STBs 104 withwhich it is in communication via a conventional communications link(whether via cable, telephone lines, satellite, the Internet or other)in a step 206.

[0043] Upon receiving the information, the STBs 104 can use theinformation to calculate profiles, provide recommendations and/orratings in a step 208. The calculation of profiles, for example, isgreatly simplified, since a useful profile can be as simple as “Contentfrom Group X accounts for N% of Viewer Y's selections/viewing time.”

[0044] Assigning Group Numbers: As noted above, this occurs at theserver. One example of the way groups could be assigned might use thefollowing organization of groups within the server:

[0045] #1 Groups With Same Name

[0046] #2 Groups for each Genre (drama, comedy, sports, history, etc.)

[0047] . . .

[0048] In this way, a taxonomy of groups can be maintained by theserver, to organize the assignment of group numbers.

[0049] At the STB: Because of the information implicit in the groupnumbers, by virtue of pre-processing by the server, the STB's tasks canbe reduced. The calculation of profiles, for example, is greatlysimplified, since a useful profile can be as simple as “Content fromGroup X accounts for N% of Viewer Y's selections/viewing time.” Thus, togenerate profiles, the STB need only execute a simplified “profilealgorithm” using the group numbers. The profile algorithm could be basedon previously gathered statistics about how much of each group (or aparticular group, or the groups of a presently available set ofprograms) a particular viewer watches; and a relative strength weightingcoefficient can be generated on the basis of those statistics.

[0050] Many other profiling algorithms using this information can alsobe used (see, for example, a discussion of profiling algorithms setforth in the Yuen WIPO publications incorporated herein by reference),and are within the scope of the present invention.

[0051] The methods and systems of the invention provide a number oftechnical advantages over prior art systems. In particular, theinvention enables an extremely fast, compact implementation ofrecommendation and other functions, suitable for limited-resourceplatforms such as STBs.

[0052] Because a significant amount of information is already implicitin the groupings (e.g., genre or other programmatic information), thereis no need to transmit such information relevant to the groupings (e.g.,whether a particular show is a comedy or a sports program). Using theserver to assign content to groups also reduces the amount ofinformation that needs to be saved or coded at the STB.

[0053] In addition, whenever necessary or desired, group assignment orother algorithmic changes can be made on the server, without thenecessity of updating the clients.

[0054] The invention also enables collaborative filtering, a method thatenables individuals to benefit from the aggregated knowledge,experience, and decision-making history of similarly-situatedindividuals. In general, collaborative filtering operates by using thedecisions a first individual makes to locate a group of otherindividuals who made similar decisions, and then using the aggregatedecisions made by the group to suggest possible future (or alternative)actions by the first individual. A well-known example of collaborativefiltering is the “Others Who Bought This Book Also Liked . . . ”recommendations from various online booksellers such as Amazon.com. Inthe context of the present invention, the STB can providerecommendations such as “Viewers who selected (or enjoyed) this contentalso selected (or enjoyed) the following programs . . . ”

[0055] As shown in FIG. 3, privacy of individuals can also be maintainedin such a system by anonymizing or stripping off and discarding personalinformation that might otherwise be transmitted by the STB prior tostorage and processing of the profile information in a step 304.Additionally, upon receiving information from the STB, the server canaverage the profile with several (or many) profiles in step 306, toerase or “blur” any suggestions of personal information and aggregateinformation in such a way as to make it impossible to track the sourceof any decision even if the content of the STB is later exposed.

[0056] Having described the illustrated embodiments of the presentinvention, it will be apparent that modifications can be made withoutdeparting from the spirit and scope of the invention, as defined by theappended claims.

1. In a content distribution architecture including a server and aclient device, a method comprising: at the server, designating a set ofgroups to which to assign content items, each group having a groupidentifier; at the server, associating with respective content items atleast one respective group identifier; and transmitting to the clientdevice a content item and associated group identifier.
 2. The method ofclaim 1 further comprising: at the client device, receiving a pluralityof content items and associated group identifiers, making the contentitems available for use by a user, and recording the associated groupidentifiers of content items selected for use by the user.
 3. The methodof claim 2 further comprising: at the client device, compilingstatistics descriptive of the group identifiers of content itemsselected for use by the user.
 4. The method of claim 3 furthercomprising: at the client device, generating at least one user profilebased on the statistics, the user profile being descriptive of thecorresponding user's patterns of content item use.
 5. The method ofclaim 3 further comprising: generating at least one content item ratingbased on the group identifiers.
 6. The method of claim 3 furthercomprising: generating at least one content item recommendation based onthe group identifiers.
 7. The method of claim 4 wherein the systemcomprises a plurality of STBs, and wherein the method further comprises:broadcasting, from the server to the client devices, a plurality ofcontent items and associated group identifier, receiving user profilesfrom the client devices, and means for generating ratings orrecommendations based on the received user profiles.
 8. The method as inany of claims 1-7 further comprising: assigning to each group acorresponding group weighting coefficient.
 9. The method as in any ofclaims 1-7 further comprising: transmitting to the STB, with the contentitem, a repeat weighting factor representative of the desirability ofrepeats for the content item.
 10. The method as in any of claims 2-7wherein neither the user profile nor the statistics descriptive ofcontent items selected by the user contain personal information of theuser.
 11. In a content distribution architecture including a server anda client device, a system comprising: means at the server fordesignating a set of groups to which to assign content items, each grouphaving a group identifier; means at the server for associating withrespective content items at least one respective group identifier; andmeans for transmitting to the client device a content item andassociated group identifier.
 12. The system of claim 11 furthercomprising: at the client device, means for receiving a plurality ofcontent items and associated group identifiers, means for making thecontent items available for use by a user, and means for recording theassociated group identifiers of content items selected for use by theuser.
 13. The system of claim 12 further comprising: at the clientdevice, means for compiling statistics descriptive of the groupidentifiers of content items selected for use by the user.
 14. Thesystem of claim 13 further comprising: at the client device, means forgenerating at least one user profile based on the statistics, the userprofile being descriptive of the corresponding user's patterns ofcontent item use.
 15. The system of claim 13 further comprising: meansfor generating at least one content item rating based on the groupidentifiers.
 16. The system of claim 13 further comprising: means forgenerating at least one content item recommendation based on the groupidentifiers.
 17. The system of claim 14 wherein the system comprises aplurality of STBs, and wherein the method further comprises: means forbroadcasting, from the server to the client devices, a plurality ofcontent items and associated group identifier, and means for receivinguser profiles from the client devices, and means for generating ratingsor recommendations based on the received user profiles.
 18. The systemas in any of claims 11-17 further comprising: means for assigning toeach group a corresponding group weighting coefficient.
 19. The systemas in any of claims 11-17 further comprising: means for transmitting tothe STB, with the content item, a repeat weighting factor representativeof the desirability of repeats for the content item.
 20. The system asin any of claims 12-17 wherein neither the user profile nor thestatistics descriptive of content items selected by the user containpersonal information of the user.